TY  - JOUR
AU  - Wu, Ke
AU  - Li, Chenya
AU  - Wang, Runming
AU  - Zhang, Canyang
AU  - Geng, Hongya
AU  - Zhou, Xuequan
AU  - He, Xiaoming
AU  - Yang, Yunfeng
AU  - Lu, Diannan
AU  - Li, Feiran
PY  - 2026
TI  - Leveraging machine learning to unravel drug–microbiome interactions in the gut
JO  - Health Engineering
SP  - 9460012
VL  - 2
AB  - The human gut microbiome (HGM) is a complex, highly diverse microbial ecosystem with extensive enzymatic and metabolic capacity. The HGM plays a pivotal role in drug biotransformation, thereby modulating the therapeutic efficacy and pharmacokinetic profiles. Considerable interindividual variability in gut microbiota composition can produce differential metabolic activity, resulting in variable therapeutic responses to the same drug across individuals. In contrast, orally administered drugs can also affect HGM diversity and composition, impacting host health. Despite its importance, the systematic investigation of drug–microbiome interactions (DMIs) presents substantial experimental challenges. In recent years, machine learning (ML) has emerged as a powerful tool in biological and health sciences, offering advanced capabilities in pattern recognition and discovery of hidden relationships. This progress opens new avenues for DMI research. This review provides an overview of recent advances in HGM-mediated drug biotransformation and drug-induced HGM modification, with particular emphasis on ML applications in these areas. We aim to elucidate the critical role of ML in the systematic exploration and mechanistic understanding of DMI, highlighting its importance as a key future research direction.
UR  - https://doi.org/10.26599/HE.2026.9460012
DO  - 10.26599/HE.2026.9460012